The amount of electronic data collected and stored in computing data stores by various entities is increasing year over year. The data collected, sometimes referred to as “big data”, may include data sets that may be beyond the ability of commonly used software tools to manage and process the data sets within a reasonable time period due to the data sets size. Data stores have been developed to better handle large data sets, such as non-relational data stores and distributed data store systems. These data stores may be used to capture and store large data sets in the areas of science, education, government and business, where persons working in these industries may encounter computing limitations when utilizing data due to the larger amount of data encountered.
As data is gathered by an increasing number of computing devices and systems, opportunities to utilize the data may also increase. For example, large amounts of financial data, geological data, Internet of Things (IoT) data, recommendation data, fraud detection, purchasing analytics, click streams, and other large data stores may be offered so that the data stores may be publicly accessible (e.g., for a fee) to parties other than the original creators of the data. However, when large data sets are available the value of the large data sets is not always clear due to issues with completeness, usefulness, age and other unexpected issues.